{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b795cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1128979b",
   "metadata": {},
   "source": [
    "# Data processing code for \"Engineering a multi-level bath for transmons with three-wave mixing and parametric drives\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be8ce81",
   "metadata": {},
   "source": [
    "In this notebook we process and analysis measurement data. \n",
    "All the experimental results provide in the paper is generated through this code. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92e2a07",
   "metadata": {},
   "source": [
    "## Imports and necessary function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dcfee218",
   "metadata": {},
   "outputs": [
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     "metadata": {
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       "id": "p1002"
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     "output_type": "display_data"
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   ],
   "source": [
    "import qutip as qt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import scipy.optimize as opt\n",
    "from numpy import linalg as LA\n",
    "import gc\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "import Gaussian_fit_library as fit\n",
    "from data_analysis_function import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08f58c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot settings\n",
    "markersize = 0.5 #1\n",
    "linewidth = 1.5 #1.5\n",
    "alpha = 0.5\n",
    "# step = 3\n",
    "\n",
    "g_color = (0/255, 174/255, 239/255)\n",
    "e_color = (57/255, 181/255, 74/255)\n",
    "f_color = (190/255, 30/255, 45/255)\n",
    "label_color = 'black'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18d4911c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clear_all():\n",
    "    \"\"\"\n",
    "        Clears all the variables from the workspace.\n",
    "    \"\"\"\n",
    "    gl = globals().copy()\n",
    "    for var in gl:\n",
    "        if var[0] == '_': continue\n",
    "        if 'func' in str(globals()[var]): continue\n",
    "        if 'module' in str(globals()[var]): continue\n",
    "\n",
    "        del globals()[var]\n",
    "\n",
    "\n",
    "def readdata(filenanme):\n",
    "    \"\"\"\n",
    "    Read the raw I and Q data from the measurement file.\n",
    "\n",
    "    Input:\n",
    "        filename (string): the path to the file for getting the raw data\n",
    "\n",
    "    Output:\n",
    "        tempIs, tempQs (numpy array): array that contains raw I and Q data from the measurement file\n",
    "    \"\"\"\n",
    "    fileSave_ = h5py.File(filenanme, 'r')\n",
    "    tempIs = fileSave_['I_data'][:]\n",
    "    tempQs = fileSave_['Q_data'][:]\n",
    "    fileSave_.close()\n",
    "    \n",
    "    return tempIs, tempQs\n",
    "\n",
    "def data_rotate(I, Q, angle):\n",
    "    \"\"\"\n",
    "    Rotate the measured I, Q data by a certain angle in the complex I-Q space.\n",
    "\n",
    "    Input:\n",
    "        I, Q (numpy array): array that contains I and Q values before rotation\n",
    "        angle (float): the value of angle for the rotation\n",
    "\n",
    "    Output:\n",
    "        new_temp.real, new_temp.imag (numpy array): the rotated I and Q value\n",
    "    \"\"\"\n",
    "    temp = I + 1j*Q\n",
    "    new_temp = temp*np.exp(-1j*angle*np.pi/180.0)\n",
    "    return new_temp.real, new_temp.imag\n",
    "    \n",
    "\n",
    "\n",
    "def population_calculation(pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe, t_i, t_f, step):   \n",
    "    \"\"\"\n",
    "    Calculate the population of each enegery level based on the three-level model given \n",
    "    in the Supplement Material Eq. S20 and Eq. S21.\n",
    "\n",
    "    Input:\n",
    "        pg_0, pe_0, pf_0 (float between 0 and 1): the intial population of the g, e, and f state\n",
    "        t_ge, teg, t_ef, t_fe (float): the transition rate of the g to e, e to g, e to f, and f to e process\n",
    "        t_i, t_f (float): time value for the inital and final point\n",
    "        step (int): number of steps in the time sweep\n",
    "    \"\"\"\n",
    "    t = np.linspace(t_i, t_f, step)   \n",
    "    dt = t[1] - t[0]\n",
    "    pg = np.zeros(len(t))\n",
    "    pe = np.zeros(len(t))\n",
    "    pf = np.zeros(len(t))\n",
    "    \n",
    "    gamma_ge = 1.0/(t_ge*1e-6)\n",
    "    gamma_eg = 1.0/(t_eg*1e-6)\n",
    "    gamma_ef = 1.0/(t_ef*1e-6)\n",
    "    gamma_fe = 1.0/(t_fe*1e-6)\n",
    "    \n",
    "    for i in range(len(t)):\n",
    "        if i==0:\n",
    "            pg[i] = pg_0\n",
    "            pe[i] = pe_0\n",
    "            pf[i] = pf_0\n",
    "        else:\n",
    "            g_rate = -pg[i-1]*gamma_ge + pe[i-1]*gamma_eg\n",
    "            e_rate = pf[i-1]*gamma_fe - pe[i-1]*(gamma_ef+gamma_eg) + pg[i-1]*gamma_ge\n",
    "            f_rate = -pf[i-1]*gamma_fe + pe[i-1]*gamma_ef\n",
    "            \n",
    "            pg[i] = pg[i-1] + g_rate*dt\n",
    "            pe[i] = pe[i-1] + e_rate*dt\n",
    "            pf[i] = pf[i-1] + f_rate*dt\n",
    "\n",
    "    return pg, pe, pf    \n",
    "\n",
    "\n",
    "\n",
    "def separate_data(I_data, Q_data, seq_num, avg_num, rotation_angle):\n",
    "\n",
    "    \"\"\"\n",
    "    The raw data contains two measurements: \n",
    "    1. A first measurement for preparing state\n",
    "    2. Second measurement for determining the state of the qubit after pump\n",
    "\n",
    "    This function is to break the raw data and group them into these two groups\n",
    "\n",
    "    Input: \n",
    "        I_data, Q_data: the I and Q component of the raw data\n",
    "        seq_num: the number of measurement sequence we are taking from the raw data\n",
    "        avg_num: the number of avgerage contained in this measurement\n",
    "        rotation_angle: the angle to rotate the data on the I-Q plane\n",
    "\n",
    "    Output:\n",
    "        testI, testQ: the raw data after rotation\n",
    "        first_result_I, first_result_Q: the first measurement data\n",
    "        result_I, result_Q: the second measurement data\n",
    "    \"\"\"\n",
    "    testI, testQ = data_rotate(I_data, Q_data, rotation_angle)\n",
    "\n",
    "    first_msmt_I = testI[0:seq_num*avg_num*2:2] \n",
    "    first_msmt_Q = testQ[0:seq_num*avg_num*2:2] \n",
    "\n",
    "    first_result_I = first_msmt_I[0:seq_num*avg_num].reshape((avg_num, seq_num))\n",
    "    first_result_Q = first_msmt_Q[0:seq_num*avg_num].reshape((avg_num, seq_num))\n",
    "\n",
    "\n",
    "    second_msmt_I = testI[1:seq_num*avg_num*2:2]\n",
    "    second_msmt_Q = testQ[1:seq_num*avg_num*2:2]\n",
    "\n",
    "    result_I = second_msmt_I[0:seq_num*avg_num].reshape((avg_num, seq_num))\n",
    "    result_Q = second_msmt_Q[0:seq_num*avg_num].reshape((avg_num, seq_num))\n",
    "\n",
    "    return testI, testQ, first_result_I, first_result_Q, result_I, result_Q\n",
    "\n",
    "def gaussian_fit_2d(testI, testQ, bin_num, lim, initial_guess, plot=True):\n",
    "    \"\"\"\n",
    "    Do a 2D Gaussian fit and the measurement histogram  to find the center of the g, e, and f state distribution\n",
    "\n",
    "    Input:\n",
    "        testI, testQ: the input I and Q data for the histogram \n",
    "        bin_num: the number of bins in the histogram \n",
    "        lim: the range limit for the I and Q value when generated the histogram \n",
    "        initial_guess: the initial guess for the fit\n",
    "        plot: to show the plot or not\n",
    "\n",
    "    Output:\n",
    "        g_center, e_center, f_center: the center of each blob for the g, e, and f distribution\n",
    "    \"\"\"\n",
    "    x = np.linspace(-lim, lim, bin_num)\n",
    "    y = np.linspace(-lim, lim, bin_num)\n",
    "    xy = np.meshgrid(x, y)\n",
    "\n",
    "    msmt_hist = np.histogram2d(testI[0:seq_num*avg_num], testQ[0:seq_num*avg_num], bins=bin_num, range=[[-lim, lim], [-lim, lim]])[0]\n",
    "    msmt_hist = msmt_hist.transpose()\n",
    "\n",
    "\n",
    "    # the input variable here are:\n",
    "    # initial_guess3 = (g blob amp, e blob amp, f blob amp, g center I, g center Q, e center I, e center Q, f center I, f center Q,\n",
    "    #                   g sigma I, g sigma Q, e sigma I, e sigma Q, f sigma I, f sigma Q)\n",
    "    initial_guess3 = initial_guess # (100, 100, 100, -158, 48, -150, -116, -2, -214, 50, 50, 50, 50, 50, 50)\n",
    "\n",
    "    fit_result = fit.fit_2D_Gaussian(xy, msmt_hist.ravel(), initial_guess3, fit.three_blob, plot=plot)\n",
    "    g_center = fit_result[3]+1j*fit_result[4]\n",
    "    e_center = fit_result[5]+1j*fit_result[6]\n",
    "    f_center = fit_result[7]+1j*fit_result[8]\n",
    "    \n",
    "    return g_center, e_center, f_center\n",
    "\n",
    "\n",
    "def get_population(first_result_I, first_result_Q, result_I, result_Q, \n",
    "                   g_center, e_center, f_center, seq_num, avg_num):\n",
    "    \n",
    "    \"\"\"\n",
    "    Process the data to get the population distribution after pump.\n",
    "\n",
    "    Input:\n",
    "        first_result_I, first_result_Q: the measured I and Q component for the first measurement,\n",
    "                                        to initialize state by selection\n",
    "        result_I, result_Q: the I and Q component for the second measurement\n",
    "        g_center, e_center, f_center: the center of each blob for the g, e, and f distribution\n",
    "        seq_num: the number of measurement sequence we are taking from the raw data\n",
    "        avg_num: the number of avgerage contained in this measurement\n",
    "\n",
    "    Output:\n",
    "        g_result, e_result, f_result: the counts for the second measurement results in g, e, and f\n",
    "    \"\"\"\n",
    "    temp_I = []\n",
    "    temp_Q = []\n",
    "\n",
    "    temp_I2 = []\n",
    "    temp_Q2 = []\n",
    "\n",
    "    temp_I3 = []\n",
    "    temp_Q3 = []\n",
    "\n",
    "\n",
    "    result = np.zeros(seq_num)\n",
    "    g_result = np.zeros(seq_num)\n",
    "    e_result = np.zeros(seq_num)\n",
    "    f_result = np.zeros(seq_num)\n",
    "\n",
    "    temp_gg_I = []\n",
    "    temp_gg_Q = []\n",
    "\n",
    "    first_gg = 0\n",
    "    first_ee = 0\n",
    "\n",
    "    first_gg_I = [] \n",
    "    first_gg_Q = []\n",
    "\n",
    "    for i in range(seq_num):\n",
    "        index = 0\n",
    "        for j in range(avg_num):\n",
    "            # calculate the data's distance from center of g, e, and f\n",
    "            test_dist = np.array([np.abs((first_result_I[j, i]+1j*first_result_Q[j, i])-g_center), np.abs((first_result_I[j, i]+1j*first_result_Q[j, i])-e_center), np.abs((first_result_I[j, i]+1j*first_result_Q[j, i])-f_center)])\n",
    "\n",
    "            # if the distance is closest to g, then we get a g state after first measurement\n",
    "            if np.argmin(test_dist) == 0:\n",
    "                first_gg += 1\n",
    "                first_gg_I.append(first_result_I[j, i])\n",
    "                first_gg_Q.append(first_result_Q[j, i])\n",
    "\n",
    "            # if the distance is closest to e, then we get a e state after first measurement\n",
    "            if np.argmin(test_dist) == 1:\n",
    "                first_ee += 1            \n",
    "\n",
    "\n",
    "            # here we only pick the data in the top right of the g blob to make sure we have the g state\n",
    "            if (first_result_I[j, i] < g_center.real) and (first_result_Q[j, i] > g_center.imag):\n",
    "                index += 1\n",
    "                if i==0:\n",
    "                    temp_I3.append(result_I[j, i])\n",
    "                    temp_Q3.append(result_Q[j, i])\n",
    "\n",
    "                dist = np.array([np.abs((result_I[j, i]+1j*result_Q[j, i])-g_center), np.abs((result_I[j, i]+1j*result_Q[j, i])-e_center), np.abs((result_I[j, i]+1j*result_Q[j, i])-f_center)])\n",
    "                if np.argmin(dist) == 0:\n",
    "                    result[i] -= 1\n",
    "                    g_result[i] += 1\n",
    "                    if i==0:\n",
    "                        temp_gg_I.append(result_I[j, i])\n",
    "                        temp_gg_Q.append(result_Q[j, i])\n",
    "                elif np.argmin(dist) == 1:\n",
    "                    result[i] += 1\n",
    "                    e_result[i] += 1\n",
    "                elif np.argmin(dist) == 2:\n",
    "                    result[i] -= 1\n",
    "                    f_result[i] += 1               \n",
    "\n",
    "\n",
    "    return g_result, e_result, f_result\n",
    "\n",
    "\n",
    "\n",
    "# theory model for the fitting\n",
    "def population_cal(t, pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe):\n",
    "    g_ge = 1/t_ge\n",
    "    g_eg = 1/t_eg\n",
    "    g_ef = 1/t_ef\n",
    "    g_fe = 1/t_fe\n",
    "    \n",
    "\n",
    "    \n",
    "    rate_array = np.array([[-g_ge, g_eg, 0], [g_ge, -g_ef-g_eg, g_fe], [0, g_ef, -g_fe]])\n",
    "    w, v = LA.eig(rate_array)\n",
    "    \n",
    "    inv_v = LA.inv(v)\n",
    "    init_condition = np.array([pg_0, pe_0, pf_0])\n",
    "    c_coeff = np.matmul(inv_v, init_condition)\n",
    "\n",
    "    t = t[0:int(len(t)/3)]\n",
    "    \n",
    "    pg = v[0, 0]*c_coeff[0]*np.exp(w[0]*t) + v[0, 1]*c_coeff[1]*np.exp(w[1]*t) + v[0, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "    pe = v[1, 0]*c_coeff[0]*np.exp(w[0]*t) + v[1, 1]*c_coeff[1]*np.exp(w[1]*t) + v[1, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "    pf = v[2, 0]*c_coeff[0]*np.exp(w[0]*t) + v[2, 1]*c_coeff[1]*np.exp(w[1]*t) + v[2, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "\n",
    "\n",
    "    return np.concatenate((pg, pe, pf))\n",
    "\n",
    "\n",
    "def plot_hist2d(I, Q, bin_num, lim):\n",
    "    \"\"\"\n",
    "    Function for plotting a 2D I-Q histogram from the input I and Q data\n",
    "\n",
    "    Input:\n",
    "        I, Q (numpy array): input data for generating the histogram\n",
    "        bin_num (int): number of bins in the 2D histogram\n",
    "        lim (float): the range of the histogram axis\n",
    "\n",
    "    Output:\n",
    "        None\n",
    "    \"\"\"\n",
    "    plt.hist2d(I, Q, bins=bin_num, range=[[-lim, lim], [-lim, lim]])\n",
    "    \n",
    "    \n",
    "    \n",
    "def batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num):\n",
    "\n",
    "    \"\"\"\n",
    "    The batch processing code for averaging the result over multiple measurement file\n",
    "\n",
    "    Input:\n",
    "        path: the path to the file\n",
    "        name: the name of the file\n",
    "        filenum: the total number of file that will be processed\n",
    "        bin_num: the number of bins in the histogram \n",
    "        lim: the range limit for the I and Q value when generated the histogram \n",
    "        rotation_angle: the angle to rotate the data on the I-Q plane\n",
    "        seq_num: the number of measurement sequence we are taking from the raw data\n",
    "        avg_num: the number of avgerage contained in this measurement\n",
    "\n",
    "    Output:\n",
    "        avg_g, avg_e, avg_f: the averaged value for Pg, Pe, and Pf\n",
    "    \"\"\"\n",
    "    avg_g = np.zeros(seq_num)\n",
    "    avg_e = np.zeros(seq_num)\n",
    "    avg_f = np.zeros(seq_num)\n",
    "\n",
    "    for i in range(filenum):\n",
    "        if np.mod(i, 5) == 0:\n",
    "            print(i)\n",
    "        # prepare data\n",
    "        raw_I_data, raw_Q_data = readdata(path+name+str(i))\n",
    "\n",
    "        # the readout signal integration window is from index 20 to index 80\n",
    "        I_data = np.mean(raw_I_data[:, 20:80], axis=1)\n",
    "        Q_data = np.mean(raw_Q_data[:, 20:80], axis=1)\n",
    "\n",
    "        tot_I, tot_Q, fisrt_I, first_Q, result_I, result_Q = separate_data(I_data, Q_data, seq_num, avg_num, rotation_angle)\n",
    "\n",
    "        # get gaussian center\n",
    "        initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2) # initial_guess for the amplitude, central position, and sigma of g, e, and f state in the 2D I-Q histogram\n",
    "        g_center, e_center, f_center = gaussian_fit_2d(tot_I, tot_Q, bin_num, lim, initial_guess, plot=False)\n",
    "\n",
    "        # get population result\n",
    "        g_result, e_result, f_result = get_population(fisrt_I, first_Q, result_I, result_Q, g_center, e_center, f_center, seq_num, avg_num)\n",
    "        tot_index = g_result+e_result+f_result\n",
    "        avg_g = avg_g + g_result/tot_index\n",
    "        avg_e = avg_e + e_result/tot_index\n",
    "        avg_f = avg_f + f_result/tot_index\n",
    "\n",
    "\n",
    "    avg_g = avg_g/filenum\n",
    "    avg_e = avg_e/filenum\n",
    "    avg_f = avg_f/filenum\n",
    "    \n",
    "    return avg_g, avg_e, avg_f\n",
    "\n",
    "def fit_pump_data(t, gdata, edata, fdata, init_guess):\n",
    "    \"\"\"\n",
    "    Fit the data to theory model\n",
    "\n",
    "    Input:\n",
    "        t: the pump durantion\n",
    "        gdata, edata, fdata: the measured data for fitting\n",
    "        init_guess: the initial guess for the fit\n",
    "\n",
    "    Output:\n",
    "        fit_result: the fit parameters\n",
    "        result: the calculated fit result\n",
    "    \"\"\"\n",
    "\n",
    "    fit_result, pcov = opt.curve_fit(population_cal, np.concatenate((t, t, t)), np.concatenate((gdata, edata, fdata)), p0=init_guess, maxfev=5000)\n",
    "    result = population_cal(np.concatenate((t, t, t)), fit_result[0], fit_result[1], fit_result[2], fit_result[3], fit_result[4], fit_result[5], fit_result[6])\n",
    "    \n",
    "    return fit_result, result\n",
    "\n",
    "# clear_all()\n",
    "\n",
    "\n",
    "def fit_pump_data_all(t, gdata1, edata1, fdata1, gdata2, edata2, fdata2, gdata3, edata3, fdata3, init_guess):\n",
    "    \"\"\"\n",
    "    Fit the g, e, and f data with different initial condition at the same time\n",
    "\n",
    "    Input: \n",
    "        t: the pump duration\n",
    "        gdata1, edata1, fdata1: the measured data for initial condition 1\n",
    "        gdata2, edata2, fdata2: the measured data for initial condition 2\n",
    "        gdata3, edata3, fdata3: the measured data for initial condition 3\n",
    "        init_guess: the initial guess for the fit\n",
    "\n",
    "    Output:\n",
    "        fit_result: the fit parameters\n",
    "        result: the calculated fit result\n",
    "    \"\"\"\n",
    "\n",
    "    fit_result, pcov = opt.curve_fit(population_cal_all, np.concatenate((t, t, t, t, t, t, t, t, t)), np.concatenate((gdata1, edata1, fdata1, gdata2, edata2, fdata2, gdata3, edata3, fdata3)), p0=init_guess, maxfev=5000)\n",
    "    result = population_cal_all(np.concatenate((t, t, t, t, t, t, t, t, t)), \n",
    "                            fit_result[0], fit_result[1],\n",
    "                            fit_result[2], fit_result[3],\n",
    "                            fit_result[4], fit_result[5],\n",
    "                            fit_result[6], fit_result[7], fit_result[8], fit_result[9])\n",
    "    \n",
    "    return fit_result, result\n",
    "\n",
    "def population_cal_all(t, pg_01, pe_01, pg_02, pe_02, pg_03, pe_03, t_ge, t_eg, t_ef, t_fe):\n",
    "    \"\"\"\n",
    "    The code for fitting the g, e, and f data with different initial condition at the same time using the semi-classical model\n",
    "    \"\"\"\n",
    "\n",
    "    g_ge = 1/t_ge\n",
    "    g_eg = 1/t_eg\n",
    "    g_ef = 1/t_ef\n",
    "    g_fe = 1/t_fe\n",
    "    \n",
    "\n",
    "    # prepare g\n",
    "    rate_array = np.array([[-g_ge, g_eg, 0], [g_ge, -g_ef-g_eg, g_fe], [0, g_ef, -g_fe]])\n",
    "    w, v = LA.eig(rate_array)\n",
    "    \n",
    "    inv_v = LA.inv(v)\n",
    "    init_condition = np.array([pg_01, pe_01, 1-pg_01-pe_01])\n",
    "    c_coeff = np.matmul(inv_v, init_condition)\n",
    "    \n",
    "    t = t[0:int(len(t)/9)]\n",
    "    \n",
    "    pg1 = v[0, 0]*c_coeff[0]*np.exp(w[0]*t) + v[0, 1]*c_coeff[1]*np.exp(w[1]*t) + v[0, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "    pe1 = v[1, 0]*c_coeff[0]*np.exp(w[0]*t) + v[1, 1]*c_coeff[1]*np.exp(w[1]*t) + v[1, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "    pf1 = v[2, 0]*c_coeff[0]*np.exp(w[0]*t) + v[2, 1]*c_coeff[1]*np.exp(w[1]*t) + v[2, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "\n",
    "\n",
    "    # prepare e\n",
    "    rate_array = np.array([[-g_ge, g_eg, 0], [g_ge, -g_ef-g_eg, g_fe], [0, g_ef, -g_fe]])\n",
    "    w, v = LA.eig(rate_array)\n",
    "    \n",
    "    inv_v = LA.inv(v)\n",
    "    init_condition = np.array([pg_02, pe_02, 1-pg_02-pe_02])\n",
    "    c_coeff = np.matmul(inv_v, init_condition)\n",
    "    \n",
    "    \n",
    "    pg2 = v[0, 0]*c_coeff[0]*np.exp(w[0]*t) + v[0, 1]*c_coeff[1]*np.exp(w[1]*t) + v[0, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "    pe2 = v[1, 0]*c_coeff[0]*np.exp(w[0]*t) + v[1, 1]*c_coeff[1]*np.exp(w[1]*t) + v[1, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "    pf2 = v[2, 0]*c_coeff[0]*np.exp(w[0]*t) + v[2, 1]*c_coeff[1]*np.exp(w[1]*t) + v[2, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "\n",
    "\n",
    "    # prepare f\n",
    "    rate_array = np.array([[-g_ge, g_eg, 0], [g_ge, -g_ef-g_eg, g_fe], [0, g_ef, -g_fe]])\n",
    "    w, v = LA.eig(rate_array)\n",
    "    \n",
    "    inv_v = LA.inv(v)\n",
    "    init_condition = np.array([pg_03, pe_03, 1-pg_03-pe_03])\n",
    "    c_coeff = np.matmul(inv_v, init_condition)\n",
    "    \n",
    "    \n",
    "    pg3 = v[0, 0]*c_coeff[0]*np.exp(w[0]*t) + v[0, 1]*c_coeff[1]*np.exp(w[1]*t) + v[0, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "    pe3 = v[1, 0]*c_coeff[0]*np.exp(w[0]*t) + v[1, 1]*c_coeff[1]*np.exp(w[1]*t) + v[1, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "    pf3 = v[2, 0]*c_coeff[0]*np.exp(w[0]*t) + v[2, 1]*c_coeff[1]*np.exp(w[1]*t) + v[2, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
    "\n",
    "    \n",
    "    return np.concatenate((pg1, pe1, pf1, pg2, pe2, pf2, pg3, pe3, pf3))\n",
    "\n",
    "# clear_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d77fb2",
   "metadata": {},
   "source": [
    "## Generate Figure 2\n",
    "### Single conversion pump\n",
    "#### Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19787ed2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# ge conversion pump\n",
    "# case 1\n",
    "path = r\"data\\ge_conversion\\\\\"\n",
    "name = \"Fig_2c_L_\"\n",
    "\n",
    "\n",
    "tot_time = 30000 # the total range of the pump time\n",
    "seq_num = 120 # total number of time point\n",
    "avg_num = 550 # number of average for each shot in each file\n",
    "filenum = 10 # how many measurement file is used\n",
    "\n",
    "lim = 10 # I and Q lim when generate measurement I-Q histogram\n",
    "bin_num = 101 # number of bins in the 2D I-Q histogram\n",
    "\n",
    "rotation_angle = -90.0 # rotation angle for better processing the IQ data\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)  \n",
    "\n",
    "avg_g_conv1, avg_e_conv1, avg_f_conv1 = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55d81240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.10409436e-01 8.56763706e-01 3.28235775e-02 9.07232421e+01\n",
      " 1.21567815e+01 5.13445881e+01 8.47210078e+00]\n"
     ]
    }
   ],
   "source": [
    "# case 1 fit\n",
    "\n",
    "# initial guess of the fit\n",
    "pg_0 = 0.0\n",
    "pe_0 = 1.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "t_ge = 9.0\n",
    "t_eg = 12\n",
    "t_ef = 51\n",
    "t_fe = 8\n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "fit_init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "conv_fit_result1, conv_result1 = fit_pump_data(t, avg_g_conv1, avg_e_conv1, avg_f_conv1, fit_init_guess)\n",
    "print(conv_fit_result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e634fbd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# case 2\n",
    "path = r\"data\\ge_conversion\\\\\"\n",
    "name = \"Fig_2c_M_\"\n",
    "\n",
    "\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "filenum = 10\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "\n",
    "avg_g_conv2, avg_e_conv2, avg_f_conv2 = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42bd7666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.80831810e-02 8.67914991e-01 4.39578272e-02 4.42873724e+01\n",
      " 3.04135122e+00 2.84763312e+07 2.71727151e+01]\n"
     ]
    }
   ],
   "source": [
    "# case 2 fit\n",
    "pg_0 = 0.0\n",
    "pe_0 = 1.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "\n",
    "t_ge = 9.0\n",
    "t_eg = 12\n",
    "t_ef = 51\n",
    "t_fe = 8\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000 \n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "fit_init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "conv_fit_result2, conv_result2 = fit_pump_data(t, avg_g_conv2, avg_e_conv2, avg_f_conv2, fit_init_guess)\n",
    "\n",
    "print(conv_fit_result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d613d064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# case 3\n",
    "path = r\"data\\ge_conversion\\\\\"\n",
    "name = \"Fig_2c_R_\"\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "filenum = 10\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "\n",
    "avg_g_conv3, avg_e_conv3, avg_f_conv3 = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d66cfe90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.91171829e-02 8.65151596e-01 3.53780395e-02 2.75437889e+01\n",
      " 1.81035399e+00 1.73748696e+02 1.55950729e+01]\n"
     ]
    }
   ],
   "source": [
    "# case 3 fit\n",
    "\n",
    "pg_0 = 0.0\n",
    "pe_0 = 1.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "\n",
    "t_ge = 9.0\n",
    "t_eg = 12\n",
    "t_ef = 51\n",
    "t_fe = 8\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000 \n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "conv_fit_result3, conv_result3 = fit_pump_data(t, avg_g_conv3, avg_e_conv3, avg_f_conv3, fit_init_guess)\n",
    "\n",
    "print(conv_fit_result3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d8fcd7",
   "metadata": {},
   "source": [
    "#### Plot the conversion result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67d62601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1012.5x225 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "markersize = 5\n",
    "linewidth = 2\n",
    "alpha = 0.5\n",
    "step = 2\n",
    "\n",
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "fig = plt.figure(figsize=(3.375*2, 1.5), layout=\"tight\")\n",
    "gs = gridspec.GridSpec(1, 3)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax1.scatter(t[0::step], avg_g_conv1[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax1.scatter(t[0::step], avg_e_conv1[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax1.scatter(t[0::step], avg_f_conv1[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax1.plot(t, conv_result1[0:int(len(conv_result1)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax1.plot(t, conv_result1[int(len(conv_result1)/3):2*int(len(conv_result1)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax1.plot(t, conv_result1[2*int(len(conv_result1)/3):3*int(len(conv_result1)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "# ax.set_ylim([-80, 1])\n",
    "ax1.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax1.set_ylabel('population', color=label_color)\n",
    "ax1.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax1.grid(linewidth=0.5)\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_yticks([0.0, 0.5, 1.0])\n",
    "ax1.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax1.set_xlim([0, 30])\n",
    "ax1.set_xticks([0, 15, 30])\n",
    "ax1.set_xticklabels([0, 15, 30])\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 1], sharey=ax1, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_conv2[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_conv2[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_conv2[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, conv_result2[0:int(len(conv_result2)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, conv_result2[int(len(conv_result2)/3):2*int(len(conv_result2)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, conv_result2[2*int(len(conv_result2)/3):3*int(len(conv_result2)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 2], sharey=ax1, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_conv3[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_conv3[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_conv3[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, conv_result3[0:int(len(conv_result3)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, conv_result3[int(len(conv_result3)/3):2*int(len(conv_result3)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, conv_result3[2*int(len(conv_result3)/3):3*int(len(conv_result3)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bb1578",
   "metadata": {},
   "source": [
    "### Single summation pump\n",
    "#### Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a6e00013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "path = r'data\\ge_summation\\\\'\n",
    "name = 'Fig_2b_L_'\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "filenum = 15#10\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "\n",
    "avg_g_sum1, avg_e_sum1, avg_f_sum1 = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d4f142c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.72549510e-01 1.98300763e-02 7.53839783e-03 1.38307392e+01\n",
      " 1.61898092e+01 4.09644428e+01 7.36112185e+00]\n"
     ]
    }
   ],
   "source": [
    "# case 1 fit\n",
    "\n",
    "pg_0 = 1.0\n",
    "pe_0 = 0.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "\n",
    "t_ge = 9.0\n",
    "t_eg = 12\n",
    "t_ef = 51\n",
    "t_fe = 8\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000 \n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "fit_init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "sum_fit_result1, sum_result1 = fit_pump_data(t, avg_g_sum1, avg_e_sum1, avg_f_sum1, fit_init_guess)\n",
    "\n",
    "print(sum_fit_result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5c93bdc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "# case 2\n",
    "\n",
    "path = r'data\\ge_summation\\\\'\n",
    "name = 'Fig_2b_M_'\n",
    "\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "filenum = 15 # 10\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "\n",
    "avg_g_sum2, avg_e_sum2, avg_f_sum2 = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d38d53d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.81623693e-01 6.63042551e-03 1.17125574e-02 4.98663004e+00\n",
      " 1.34311755e+01 5.08098978e+01 9.50891516e+00]\n"
     ]
    }
   ],
   "source": [
    "# case 2 fit\n",
    "\n",
    "pg_0 = 1.0\n",
    "pe_0 = 0.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "\n",
    "t_ge = 9.0\n",
    "t_eg = 12\n",
    "t_ef = 51\n",
    "t_fe = 8\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000 \n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "fit_init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "sum_fit_result2, sum_result2 = fit_pump_data(t, avg_g_sum2, avg_e_sum2, avg_f_sum2, fit_init_guess)\n",
    "\n",
    "print(sum_fit_result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bec9cb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# case 3\n",
    "\n",
    "path = r'data\\ge_summation\\\\'\n",
    "name = 'Fig_2b_R_'\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "filenum = 10\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "\n",
    "avg_g_sum3, avg_e_sum3, avg_f_sum3 = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "f2cd7f59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.11320268e+00 -1.35183246e-01  2.19567553e-02  6.10885311e-01\n",
      "  3.89795421e+00  4.46317295e+01  7.94839244e+00]\n"
     ]
    }
   ],
   "source": [
    "# case 3 fit\n",
    "\n",
    "pg_0 = 1.0\n",
    "pe_0 = 0.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "\n",
    "t_ge = 9.0\n",
    "t_eg = 12\n",
    "t_ef = 51\n",
    "t_fe = 8\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000 \n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "fit_init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "sum_fit_result3, sum_result3 = fit_pump_data(t, avg_g_sum3, avg_e_sum3, avg_f_sum3, fit_init_guess)\n",
    "\n",
    "print(sum_fit_result3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566b46d1",
   "metadata": {},
   "source": [
    "#### Plot the summation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d36bad57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1012.5x225 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "markersize = 5\n",
    "linewidth = 2\n",
    "alpha = 0.5\n",
    "step = 2\n",
    "\n",
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "fig = plt.figure(figsize=(3.375*2, 1.5), layout=\"tight\")\n",
    "gs = gridspec.GridSpec(1, 3)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax1.scatter(t[0::step], avg_g_sum1[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax1.scatter(t[0::step], avg_e_sum1[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax1.scatter(t[0::step], avg_f_sum1[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax1.plot(t, sum_result1[0:int(len(sum_result1)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax1.plot(t, sum_result1[int(len(sum_result1)/3):2*int(len(sum_result1)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax1.plot(t, sum_result1[2*int(len(sum_result1)/3):3*int(len(sum_result1)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "\n",
    "ax1.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax1.set_ylabel('population', color=label_color)\n",
    "ax1.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax1.grid(linewidth=0.5)\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_yticks([0.0, 0.5, 1.0])\n",
    "ax1.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax1.set_xlim([0, 30])\n",
    "ax1.set_xticks([0, 15, 30])\n",
    "ax1.set_xticklabels([0, 15, 30])\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 1], sharey=ax1, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_sum2[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_sum2[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_sum2[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, sum_result2[0:int(len(sum_result2)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, sum_result2[int(len(sum_result2)/3):2*int(len(sum_result2)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, sum_result2[2*int(len(sum_result2)/3):3*int(len(sum_result2)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 2], sharey=ax1, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_sum3[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_sum3[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_sum3[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, sum_result3[0:int(len(sum_result3)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, sum_result3[int(len(sum_result3)/3):2*int(len(sum_result3)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, sum_result3[2*int(len(sum_result3)/3):3*int(len(sum_result3)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010aadf5",
   "metadata": {},
   "source": [
    "### Final figure for figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66322a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 506.25x330 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "markersize = 1\n",
    "linewidth = 1.5\n",
    "alpha = 0.5\n",
    "step = 2\n",
    "\n",
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "fig = plt.figure(figsize=(3.375, 2.2), layout=\"tight\")\n",
    "gs = gridspec.GridSpec(2, 3)\n",
    "\n",
    "ax2 = fig.add_subplot(gs[0, 0])\n",
    "ax2.scatter(t[0::step], avg_g_sum1[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax2.scatter(t[0::step], avg_e_sum1[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax2.scatter(t[0::step], avg_f_sum1[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax2.plot(t, sum_result1[0:int(len(sum_result1)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax2.plot(t, sum_result1[int(len(sum_result1)/3):2*int(len(sum_result1)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax2.plot(t, sum_result1[2*int(len(sum_result1)/3):3*int(len(sum_result1)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "\n",
    "ax2.set_ylabel('population', color=label_color)\n",
    "ax2.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax2.grid(linewidth=0.5)\n",
    "ax2.set_ylim([0, 1])\n",
    "ax2.set_yticks([0.0, 0.5, 1.0])\n",
    "ax2.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax2.set_xlim([0, 30])\n",
    "ax2.set_xticks([0, 15, 30])\n",
    "ax2.set_xticklabels([0, 15, 30])\n",
    "plt.setp(ax2.get_xticklabels(), visible=False)\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 1], sharey=ax2, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_sum2[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_sum2[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_sum2[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, sum_result2[0:int(len(sum_result2)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, sum_result2[int(len(sum_result2)/3):2*int(len(sum_result2)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, sum_result2[2*int(len(sum_result2)/3):3*int(len(sum_result2)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "plt.setp(ax.get_xticklabels(), visible=False)\n",
    "plt.setp(ax.get_yticklabels(), visible=False)\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 2], sharey=ax2, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_sum3[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_sum3[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_sum3[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, sum_result3[0:int(len(sum_result3)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, sum_result3[int(len(sum_result3)/3):2*int(len(sum_result3)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, sum_result3[2*int(len(sum_result3)/3):3*int(len(sum_result3)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "plt.setp(ax.get_xticklabels(), visible=False)\n",
    "plt.setp(ax.get_yticklabels(), visible=False)\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(gs[1, 0])\n",
    "ax1.scatter(t[0::step], avg_g_conv1[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax1.scatter(t[0::step], avg_e_conv1[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax1.scatter(t[0::step], avg_f_conv1[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax1.plot(t, conv_result1[0:int(len(conv_result1)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax1.plot(t, conv_result1[int(len(conv_result1)/3):2*int(len(conv_result1)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax1.plot(t, conv_result1[2*int(len(conv_result1)/3):3*int(len(conv_result1)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax1.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax1.set_ylabel('population', color=label_color)\n",
    "ax1.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax1.grid(linewidth=0.5)\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_yticks([0.0, 0.5, 1.0])\n",
    "ax1.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax1.set_xlim([0, 30])\n",
    "ax1.set_xticks([0, 15, 30])\n",
    "ax1.set_xticklabels([0, 15, 30])\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[1, 1], sharey=ax2, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_conv2[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_conv2[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_conv2[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, conv_result2[0:int(len(conv_result2)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, conv_result2[int(len(conv_result2)/3):2*int(len(conv_result2)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, conv_result2[2*int(len(conv_result2)/3):3*int(len(conv_result2)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "plt.setp(ax.get_yticklabels(), visible=False)\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[1, 2], sharey=ax2, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_conv3[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_conv3[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_conv3[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, conv_result3[0:int(len(conv_result3)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, conv_result3[int(len(conv_result3)/3):2*int(len(conv_result3)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, conv_result3[2*int(len(conv_result3)/3):3*int(len(conv_result3)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f^+ \\rangle$')\n",
    "\n",
    "ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "plt.setp(ax.get_yticklabels(), visible=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba64391f",
   "metadata": {},
   "source": [
    "## Generate Figure 3\n",
    "### Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ba3bcd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "path = r'data\\ge_eg_balance\\\\'\n",
    "name = 'Fig_3c_U_'\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10 #400\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "file_num = 15\n",
    "\n",
    "avg_g = np.zeros(seq_num)\n",
    "avg_e = np.zeros(seq_num)\n",
    "avg_f = np.zeros(seq_num)\n",
    "\n",
    "for i in range(file_num):\n",
    "    if np.mod(i, 5) == 0:\n",
    "        print(i)\n",
    "    # prepare data\n",
    "    raw_I_data, raw_Q_data = readdata(path+name+str(i))\n",
    "    I_data = np.mean(raw_I_data[:, 20:80], axis=1)\n",
    "    Q_data = np.mean(raw_Q_data[:, 20:80], axis=1)\n",
    "    \n",
    "    tot_I, tot_Q, fisrt_I, first_Q, result_I, result_Q = separate_data(I_data, Q_data, seq_num, avg_num, rotation_angle)\n",
    "    \n",
    "    # get gaussian center\n",
    "    initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "    g_center, e_center, f_center = gaussian_fit_2d(tot_I, tot_Q, bin_num, lim, initial_guess, plot=False)\n",
    "    \n",
    "    # get population result\n",
    "    g_result, e_result, f_result = get_population(fisrt_I, first_Q, result_I, result_Q, g_center, e_center, f_center, seq_num, avg_num)\n",
    "    tot_index = g_result+e_result+f_result\n",
    "    avg_g = avg_g + g_result/tot_index\n",
    "    avg_e = avg_e + e_result/tot_index\n",
    "    avg_f = avg_f + f_result/tot_index\n",
    "\n",
    "    \n",
    "ge_eg_avg_g = avg_g/file_num\n",
    "ge_eg_avg_e = avg_e/file_num\n",
    "ge_eg_avg_f = avg_f/file_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "045790a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.57149467e-01 2.84851235e-02 1.43490964e-02 2.92337366e+00\n",
      " 2.93136084e+00 4.29517072e+01 1.07357845e+01]\n"
     ]
    }
   ],
   "source": [
    "pg_0 = 1.0\n",
    "pe_0 = 0.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "\n",
    "t_ge = 1.73\n",
    "t_eg = 15.0\n",
    "t_ef = 175.95\n",
    "t_fe = 10.0\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000\n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "\n",
    "ge_eg_fit_result, ge_eg_pcov = opt.curve_fit(population_cal, np.concatenate((t, t, t)), np.concatenate((ge_eg_avg_g, ge_eg_avg_e, ge_eg_avg_f)), p0=init_guess, maxfev=5000)\n",
    "\n",
    "ge_eg_result = population_cal(np.concatenate((t, t, t)), ge_eg_fit_result[0], ge_eg_fit_result[1], ge_eg_fit_result[2], ge_eg_fit_result[3], ge_eg_fit_result[4], ge_eg_fit_result[5], ge_eg_fit_result[6])\n",
    "\n",
    "print(ge_eg_fit_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4cefd1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "path = r'data\\ge_eg_balance\\\\'\n",
    "name = 'Fig_3c_B_'\n",
    "\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10 \n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "\n",
    "avg_g = np.zeros(seq_num)\n",
    "avg_e = np.zeros(seq_num)\n",
    "avg_f = np.zeros(seq_num)\n",
    "\n",
    "for i in range(15):\n",
    "    if np.mod(i, 5) == 0:\n",
    "        print(i)\n",
    "    # prepare data\n",
    "    raw_I_data, raw_Q_data = readdata(path+name+str(i))\n",
    "    I_data = np.mean(raw_I_data[:, 20:80], axis=1)\n",
    "    Q_data = np.mean(raw_Q_data[:, 20:80], axis=1)\n",
    "    \n",
    "    tot_I, tot_Q, fisrt_I, first_Q, result_I, result_Q = separate_data(I_data, Q_data, seq_num, avg_num, rotation_angle)\n",
    "    \n",
    "    # get gaussian center\n",
    "    initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "    g_center, e_center, f_center = gaussian_fit_2d(tot_I, tot_Q, bin_num, lim, initial_guess, plot=False)\n",
    "    \n",
    "    # get population result\n",
    "    g_result, e_result, f_result = get_population(fisrt_I, first_Q, result_I, result_Q, g_center, e_center, f_center, seq_num, avg_num)\n",
    "    tot_index = g_result+e_result+f_result\n",
    "    avg_g = avg_g + g_result/tot_index\n",
    "    avg_e = avg_e + e_result/tot_index\n",
    "    avg_f = avg_f + f_result/tot_index\n",
    "\n",
    "    \n",
    "ge_eg_avg_g2 = avg_g/15\n",
    "ge_eg_avg_e2 = avg_e/15\n",
    "ge_eg_avg_f2 = avg_f/15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35ea8e23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.21053577e-01 6.61663023e-02 1.27506671e-02 6.70563775e+00\n",
      " 7.19235474e+00 4.60466966e+01 1.15950510e+01]\n"
     ]
    }
   ],
   "source": [
    "pg_0 = 1.0\n",
    "pe_0 = 0.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "\n",
    "t_ge = 1.73\n",
    "t_eg = 15.0\n",
    "t_ef = 175.95\n",
    "t_fe = 10.0\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000\n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "\n",
    "ge_eg_fit_result2, ge_eg_pcov2 = opt.curve_fit(population_cal, np.concatenate((t, t, t)), np.concatenate((ge_eg_avg_g2, ge_eg_avg_e2, ge_eg_avg_f2)), p0=init_guess, maxfev=5000)\n",
    "\n",
    "ge_eg_result2 = population_cal(np.concatenate((t, t, t)), ge_eg_fit_result2[0], ge_eg_fit_result2[1], ge_eg_fit_result2[2], ge_eg_fit_result2[3], ge_eg_fit_result2[4], ge_eg_fit_result2[5], ge_eg_fit_result2[6])\n",
    "\n",
    "print(ge_eg_fit_result2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3b4737",
   "metadata": {},
   "source": [
    "### Final figure for figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc4e6496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'), Text(15, 0, '15'), Text(30, 0, '30')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 337.5x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "step = 2\n",
    "\n",
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "fig, ax0 = plt.subplots(2, 1, figsize=(3.375*2/3, 1.8), sharex=True)\n",
    "\n",
    "ax = ax0[0]\n",
    "ax1 = ax0[1]\n",
    "\n",
    "\n",
    "ax.scatter(t[0::step], ge_eg_avg_g[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], ge_eg_avg_e[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], ge_eg_avg_f[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "ax.plot(t, ge_eg_result[0:int(len(ge_eg_result)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, ge_eg_result[int(len(ge_eg_result)/3):2*int(len(ge_eg_result)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, ge_eg_result[2*int(len(ge_eg_result)/3):3*int(len(ge_eg_result)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_ylabel('population', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "\n",
    "\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_yticks([0.0, 0.5, 1.0])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "\n",
    "ax1.scatter(t[0::step], ge_eg_avg_g2[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax1.scatter(t[0::step], ge_eg_avg_e2[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax1.scatter(t[0::step], ge_eg_avg_f2[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax1.plot(t, ge_eg_result2[0:int(len(ge_eg_result2)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax1.plot(t, ge_eg_result2[int(len(ge_eg_result2)/3):2*int(len(ge_eg_result2)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax1.plot(t, ge_eg_result2[2*int(len(ge_eg_result2)/3):3*int(len(ge_eg_result2)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax1.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax1.set_ylabel('population', color=label_color)\n",
    "ax1.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax1.grid(linewidth=0.5)\n",
    "\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_yticks([0.0, 0.5, 1.0])\n",
    "ax1.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax1.set_xlim([0, 30])\n",
    "ax1.set_xticks([0, 15, 30])\n",
    "ax1.set_xticklabels([0, 15, 30])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b6c2054",
   "metadata": {},
   "source": [
    "## Generate Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e53387c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n",
      "10\n",
      "15\n",
      "20\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "path = r'data\\three_level_pump\\\\'\n",
    "name = 'Fig_4_'\n",
    "\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10 #400\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "\n",
    "avg_g = np.zeros(seq_num)\n",
    "avg_e = np.zeros(seq_num)\n",
    "avg_f = np.zeros(seq_num)\n",
    "\n",
    "for i in range(30):\n",
    "    if np.mod(i, 5) == 0:\n",
    "        print(i)\n",
    "    # prepare data\n",
    "    raw_I_data, raw_Q_data = readdata(path+name+str(i))\n",
    "    I_data = np.mean(raw_I_data[:, 10:60], axis=1)\n",
    "    Q_data = np.mean(raw_Q_data[:, 10:60], axis=1)\n",
    "    \n",
    "    \n",
    "    tot_I, tot_Q, fisrt_I, first_Q, result_I, result_Q = separate_data(I_data, Q_data, seq_num, avg_num, rotation_angle)\n",
    "    \n",
    "    # get gaussian center\n",
    "    initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "    g_center, e_center, f_center = gaussian_fit_2d(tot_I, tot_Q, bin_num, lim, initial_guess, plot=False)\n",
    "    \n",
    "    # get population result\n",
    "    g_result, e_result, f_result = get_population(fisrt_I, first_Q, result_I, result_Q, g_center, e_center, f_center, seq_num, avg_num)\n",
    "    tot_index = g_result+e_result+f_result\n",
    "    avg_g = avg_g + g_result/tot_index\n",
    "    avg_e = avg_e + e_result/tot_index\n",
    "    avg_f = avg_f + f_result/tot_index\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1d740b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# average over file number\n",
    "gef_avg_g = avg_g/30\n",
    "gef_avg_e = avg_e/30\n",
    "gef_avg_f = avg_f/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfd680c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hamiltonian model for the 3-level system\n",
    "\n",
    "def decay_calculate_with_msmt_decay(t, pg0, pe0, drive1, drive2, drive3):\n",
    "    opts = qt.Options(store_states=True)\n",
    "    s_dim = 4\n",
    "    q_dim = 4\n",
    "    a = qt.tensor(qt.destroy(s_dim), qt.qeye(q_dim))\n",
    "    b = qt.tensor(qt.qeye(s_dim), qt.destroy(q_dim))\n",
    "    \n",
    "    g_qqqq = 197e6*2*np.pi/1e9 * 1e3\n",
    "    kappa_a = 12.98e-3*2*np.pi * 1e3\n",
    "\n",
    "    kappa_b = 18e-5 *1e3 \n",
    "    kappa_b_up = kappa_b*0.2\n",
    "\n",
    "\n",
    "    H0 = -g_qqqq/2*(b.dag()*b.dag()*b*b) \n",
    "    H1 = a*b\n",
    "    H2 = a.dag()*b.dag()\n",
    "    H3 = a*b.dag()\n",
    "    H4 = a.dag()*b\n",
    "    \n",
    "    drive1 = drive1\n",
    "    drive2 = drive2\n",
    "    drive3 = drive3\n",
    "    msmt_time = 1200*1e-3 \n",
    "    \n",
    "    \n",
    "    def H1_coeff(t, args):\n",
    "        return drive1*np.exp(1j*drive_frequency1*t) \n",
    "    \n",
    "    def H2_coeff(t, args):\n",
    "        return drive1*np.exp(-1j*drive_frequency1*t) \n",
    "    \n",
    "    def H3_coeff(t, args):\n",
    "        return drive2*np.exp(1j*drive_frequency2*t)\n",
    "    \n",
    "    def H4_coeff(t, args):\n",
    "        return drive2*np.exp(-1j*drive_frequency2*t)\n",
    "\n",
    "    \n",
    "    # ef drive is one anharmonicty detuned in the rotating frame of ge drive\n",
    "    drive_frequency1 = -g_qqqq\n",
    "    drive_frequency2 = 0*1e6*2*np.pi/1e9 *1e3\n",
    "    \n",
    "    H0 = -g_qqqq/2*(b.dag()*b.dag()*b*b) \n",
    "    H = [H0, [H1, H1_coeff], [H2, H2_coeff], [H3, H3_coeff], [H4, H4_coeff]]\n",
    "    \n",
    "    # make initial state\n",
    "    pf0 = 1.0 - pg0 - pe0\n",
    "    a0 = qt.basis(s_dim, 0)*qt.basis(s_dim, 0).dag()\n",
    "    b0 = pg0*qt.basis(q_dim, 0)*qt.basis(q_dim, 0).dag() + pe0*qt.basis(q_dim, 1)*qt.basis(q_dim, 1).dag() + pf0*qt.basis(q_dim, 2)*qt.basis(q_dim, 2).dag()\n",
    "    psi0 = qt.tensor(a0, b0)\n",
    "    \n",
    "    q_g = qt.tensor(qt.qeye(s_dim), qt.basis(q_dim, 0)*qt.basis(q_dim, 0).dag())\n",
    "    q_e = qt.tensor(qt.qeye(s_dim), qt.basis(q_dim, 1)*qt.basis(q_dim, 1).dag())\n",
    "    \n",
    "    c_ops = [np.sqrt(kappa_a)*a, np.sqrt(kappa_b)*b, np.sqrt(kappa_b_up)*b.dag()]\n",
    "    output = qt.mesolve(H, psi0, t, c_ops, [q_g, q_e], options=opts)\n",
    "    \n",
    "\n",
    "\n",
    "    # take into account the qubit decay during the measurement\n",
    "    pg_c = output.expect[0]\n",
    "    pe_c = output.expect[1]\n",
    "    pf_c = 1-output.expect[0]-output.expect[1]  \n",
    "\n",
    "    for i in range(len(t)):\n",
    "        if np.mod(i, 100) == 0:\n",
    "            print(i)\n",
    "        t_new = np.linspace(0, msmt_time, 51)\n",
    "        psi0 = output.states[i]\n",
    "        output1 = qt.mesolve(H0, psi0, t_new, c_ops, [q_g, q_e])\n",
    "        pg_c[i] = output1.expect[0][-1]\n",
    "        pe_c[i] = output1.expect[1][-1]\n",
    "        pf_c[i] = 1 - pg_c[i] - pe_c[i]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    return np.concatenate((pg_c, pe_c, pf_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1068b27f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\xicao\\Miniconda3\\envs\\labcore\\lib\\site-packages\\qutip\\solver\\options.py:16: FutureWarning: Dedicated options class are no longer needed, options should be passed as dict to solvers.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n"
     ]
    }
   ],
   "source": [
    "a = 2.5\n",
    "b = a*1.7\n",
    "\n",
    "t1 = np.linspace(0, 30000, 501)*1e-3\n",
    "result = decay_calculate_with_msmt_decay(t1, 1.0, 0.0, a*2*np.pi*1.0, b*2*np.pi, 0.0*1e-3*2*np.pi)\n",
    "\n",
    "pg_c = result[0:int(len(result)/3)]\n",
    "pe_c = result[int(len(result)/3):2*int(len(result)/3)]\n",
    "pf_c = result[2*int(len(result)/3):3*int(len(result)/3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c08912c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'time ($\\\\mu$s)')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 506.25x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t1 = np.linspace(0, 30000, 501)[::1] *1e-3\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000 \n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "fig = plt.figure(figsize=(3.375, 2.2), layout=\"tight\")\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "\n",
    "step = 1\n",
    "ax2 = fig.add_subplot(gs[0, 0])\n",
    "ax2.scatter(t[0::step], gef_avg_g[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax2.scatter(t[0::step], gef_avg_e[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax2.scatter(t[0::step], gef_avg_f[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax2.plot(t1, result[0:int(len(result)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax2.plot(t1, result[int(len(result)/3):2*int(len(result)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax2.plot(t1, result[2*int(len(result)/3):3*int(len(result)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax2.set_ylabel('population', color=label_color)\n",
    "ax2.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax2.grid(linewidth=0.5)\n",
    "ax2.set_ylim([0, 1])\n",
    "ax2.set_yticks([0.0, 0.5, 1.0])\n",
    "ax2.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax2.set_xlim([0, 30])\n",
    "ax2.set_xticks([0, 15, 30])\n",
    "ax2.set_xticklabels([0, 15, 30])\n",
    "ax2.set_xlabel(r'time ($\\mu$s)', color=label_color)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbe0806",
   "metadata": {},
   "source": [
    "### Generate Supplement Figure S6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e779ba3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00000000e+00 3.52138390e-33 2.03038790e-43 1.75889252e+00\n",
      " 6.81185748e-01 1.77656559e-01 4.78425177e-01]\n"
     ]
    }
   ],
   "source": [
    "# inital guess of the fitting parameters\n",
    "pg_0 = 1.0\n",
    "pe_0 = 0.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "t_ge = 2\n",
    "t_eg = 1\n",
    "t_ef = 1\n",
    "t_fe = 2\n",
    "\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000\n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "\n",
    "gef_fit_result, ge_eg_pcov = opt.curve_fit(population_cal, np.concatenate((t, t, t)), np.concatenate((gef_avg_g, gef_avg_e, gef_avg_f)), p0=init_guess, maxfev=6000, bounds=((0, 0, 0, 0, 0, 0, 0), (1, 1, 1, np.inf, np.inf, np.inf, np.inf)))\n",
    "\n",
    "gef_result = population_cal(np.concatenate((t, t, t)), gef_fit_result[0], gef_fit_result[1], gef_fit_result[2], gef_fit_result[3], gef_fit_result[4], gef_fit_result[5], gef_fit_result[6])\n",
    "\n",
    "print(gef_fit_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6c68659b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 506.25x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "fig = plt.figure(figsize=(3.375, 3), layout=\"tight\")\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "\n",
    "step = 1\n",
    "ax2 = fig.add_subplot(gs[0, 0])\n",
    "ax2.scatter(t[0::step], gef_avg_g[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax2.scatter(t[0::step], gef_avg_e[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax2.scatter(t[0::step], gef_avg_f[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax2.plot(t, gef_result[0:int(len(gef_result)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax2.plot(t, gef_result[int(len(gef_result)/3):2*int(len(gef_result)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax2.plot(t, gef_result[2*int(len(gef_result)/3):3*int(len(gef_result)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax2.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax2.set_ylabel('population', color=label_color)\n",
    "ax2.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax2.grid(linewidth=0.5)\n",
    "ax2.set_ylim([0, 1])\n",
    "ax2.set_yticks([0.0, 0.5, 1.0])\n",
    "ax2.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax2.set_xlim([0, 10])\n",
    "ax2.set_xticks([0, 5, 10])\n",
    "ax2.set_xticklabels([0, 5, 10])\n",
    "plt.setp(ax2.get_xticklabels(), visible=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3167e30c",
   "metadata": {},
   "source": [
    "## Generate Supplement Figure S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "ed2875f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# g prep case 1\n",
    "\n",
    "path = r'data\\natural_decay\\\\'\n",
    "name = '001_prep_g_natural_decay_'\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 30000\n",
    "lim = 10\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "filenum = 10\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "avg_g_gprep, avg_e_gprep, avg_f_gprep = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aaf4e1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# e prep case 2\n",
    "\n",
    "path = r'data\\natural_decay\\\\'\n",
    "name = '001_prep_e_natural_decay_'\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "filenum = 10\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "\n",
    "avg_g_eprep, avg_e_eprep, avg_f_eprep = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f0993cb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "#f prep  case 3\n",
    "\n",
    "path = r'data\\natural_decay\\\\'\n",
    "name = '001_prep_f_natural_decay_'\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 120000\n",
    "lim = 10\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "filenum = 10\n",
    "initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "\n",
    "avg_g_fprep, avg_e_fprep, avg_f_fprep = batch_process(path, name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b858163e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gamma ge 1  1.1676821927042043\n",
      "Gamma eg 1  8.347092499443992\n",
      "Gamma ef 1  2.9450625896514095\n",
      "Gamma fe 1  13.087516141510699\n"
     ]
    }
   ],
   "source": [
    "# fit everything together\n",
    "pg_01 = 1.0\n",
    "pe_01 = 0.0\n",
    "pf_01 = 0.0\n",
    "\n",
    "pg_02 = 0.0\n",
    "pe_02 = 1.0\n",
    "pf_02 = 0.0\n",
    "\n",
    "\n",
    "pg_03 = 0.0\n",
    "pe_03 = 0.0\n",
    "pf_03 = 1.0\n",
    "\n",
    "\n",
    "t_ge = 75\n",
    "t_eg = 17\n",
    "t_ef = 36\n",
    "t_fe = 11\n",
    "\n",
    "fit_init_guess = (pg_01, pe_01,\n",
    "              pg_02, pe_02, \n",
    "              pg_03, pe_03, \n",
    "              t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "all_prep_fit_result, all_prep_result = fit_pump_data_all(t, \n",
    "                                                         avg_g_gprep, avg_e_gprep, avg_f_gprep, \n",
    "                                                         avg_g_eprep, avg_e_eprep, avg_f_eprep, \n",
    "                                                         avg_g_fprep, avg_e_fprep, avg_f_fprep, fit_init_guess)\n",
    "\n",
    "t_ge_fit_1 = all_prep_fit_result[6]\n",
    "gamma_ge_fit_1 = 1.0/(t_ge_fit_1*2*np.pi)\n",
    "\n",
    "t_eg_fit_1 = all_prep_fit_result[7]\n",
    "gamma_eg_fit_1 = 1.0/(t_eg_fit_1*2*np.pi)\n",
    "\n",
    "t_ef_fit_1 = all_prep_fit_result[8]\n",
    "gamma_ef_fit_1 = 1.0/(t_ef_fit_1*2*np.pi)\n",
    "\n",
    "t_fe_fit_1 = all_prep_fit_result[9]\n",
    "gamma_fe_fit_1 = 1.0/(t_fe_fit_1*2*np.pi)\n",
    "print(\"Gamma ge 1 \", gamma_ge_fit_1*1e3)\n",
    "print(\"Gamma eg 1 \", gamma_eg_fit_1*1e3)\n",
    "print(\"Gamma ef 1 \", gamma_ef_fit_1*1e3)\n",
    "print(\"Gamma fe 1 \", gamma_fe_fit_1*1e3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "0e5dccfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 506.25x225 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "markersize = 0.5\n",
    "linewidth = 1.5\n",
    "alpha = 0.5\n",
    "step = 3\n",
    "\n",
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "fig = plt.figure(figsize=(3.375, 1.5), layout=\"tight\")\n",
    "gs = gridspec.GridSpec(1, 3)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax1.scatter(t[0::step], avg_g_gprep[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax1.scatter(t[0::step], avg_e_gprep[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax1.scatter(t[0::step], avg_f_gprep[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax1.plot(t, all_prep_result[0:int(len(all_prep_result)/9)], color=g_color, linewidth=linewidth, label=r'$| g \\rangle$', alpha=alpha)\n",
    "ax1.plot(t, all_prep_result[int(len(all_prep_result)/9):2*int(len(all_prep_result)/9)], color=e_color, linewidth=linewidth, label=r'$| e \\rangle$', alpha=alpha)\n",
    "ax1.plot(t, all_prep_result[2*int(len(all_prep_result)/9):3*int(len(all_prep_result)/9)], color=f_color, linewidth=linewidth, label=r'$| f \\rangle$', alpha=alpha)\n",
    "\n",
    "\n",
    "# ax.set_ylim([-80, 1])\n",
    "# ax1.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax1.set_ylabel('population', color=label_color)\n",
    "ax1.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax1.grid(linewidth=0.5)\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_yticks([0.0, 0.5, 1.0])\n",
    "ax1.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax1.set_xlim([0, 30])\n",
    "ax1.set_xticks([0, 15, 30])\n",
    "ax1.set_xticklabels([0, 15, 30])\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 1], sharey=ax1, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_eprep[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_eprep[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_eprep[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, all_prep_result[3*int(len(all_prep_result)/9):4*int(len(all_prep_result)/9)], color=g_color, linewidth=linewidth, label=r'$| g \\rangle$', alpha=alpha)\n",
    "ax.plot(t, all_prep_result[4*int(len(all_prep_result)/9):5*int(len(all_prep_result)/9)], color=e_color, linewidth=linewidth, label=r'$| e \\rangle$', alpha=alpha)\n",
    "ax.plot(t, all_prep_result[5*int(len(all_prep_result)/9):6*int(len(all_prep_result)/9)], color=f_color, linewidth=linewidth, label=r'$| f \\rangle$', alpha=alpha)\n",
    "\n",
    "ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "plt.setp(ax.get_yticklabels(), visible=False)\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 2], sharey=ax1, sharex=ax1)\n",
    "ax.scatter(t[0::step], avg_g_fprep[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_e_fprep[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], avg_f_fprep[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, all_prep_result[6*int(len(all_prep_result)/9):7*int(len(all_prep_result)/9)], color=g_color, linewidth=linewidth, label=r'$| g \\rangle$', alpha=alpha)\n",
    "ax.plot(t, all_prep_result[7*int(len(all_prep_result)/9):8*int(len(all_prep_result)/9)], color=e_color, linewidth=linewidth, label=r'$| e \\rangle$', alpha=alpha)\n",
    "ax.plot(t, all_prep_result[8*int(len(all_prep_result)/9):9*int(len(all_prep_result)/9)], color=f_color, linewidth=linewidth, label=r'$| f \\rangle$', alpha=alpha)\n",
    "\n",
    "# ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "plt.setp(ax.get_yticklabels(), visible=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc5eb4b",
   "metadata": {},
   "source": [
    "## Generate Supplement Figure S4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76f932fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# e higher than g\n",
    "path = r\"data\\ge_eg_multi_pump\\\\\"\n",
    "name = 'Fig_S4_L_'\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 30000\n",
    "lim = 10 #400\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "\n",
    "avg_g = np.zeros(seq_num)\n",
    "avg_e = np.zeros(seq_num)\n",
    "avg_f = np.zeros(seq_num)\n",
    "\n",
    "file_num = 1\n",
    "for i in range(file_num):\n",
    "    if np.mod(i, 5) == 0:\n",
    "        print(i)\n",
    "    # prepare data\n",
    "    raw_I_data, raw_Q_data = readdata(path+name+str(i))\n",
    "    I_data = np.mean(raw_I_data[:, 20:80], axis=1)\n",
    "    Q_data = np.mean(raw_Q_data[:, 20:80], axis=1)\n",
    "    \n",
    "    tot_I, tot_Q, fisrt_I, first_Q, result_I, result_Q = separate_data(I_data, Q_data, seq_num, avg_num, rotation_angle)\n",
    "    \n",
    "    # get gaussian center\n",
    "    initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "    g_center, e_center, f_center = gaussian_fit_2d(tot_I, tot_Q, bin_num, lim, initial_guess, plot=False)\n",
    "    \n",
    "    # get population result\n",
    "    g_result, e_result, f_result = get_population(fisrt_I, first_Q, result_I, result_Q, g_center, e_center, f_center, seq_num, avg_num)\n",
    "    tot_index = g_result+e_result+f_result\n",
    "    avg_g = avg_g + g_result/tot_index\n",
    "    avg_e = avg_e + e_result/tot_index\n",
    "    avg_f = avg_f + f_result/tot_index\n",
    "\n",
    "    \n",
    "ge_eg_avg_g3 = avg_g/file_num\n",
    "ge_eg_avg_e3 = avg_e/file_num\n",
    "ge_eg_avg_f3 = avg_f/file_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "307836ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.69175165e-01 2.18880195e-02 8.94582225e-03 1.34499493e+00\n",
      " 4.43301619e+00 3.77856720e+01 1.10924324e+01]\n"
     ]
    }
   ],
   "source": [
    "pg_0 = 1.0\n",
    "pe_0 = 0.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "t_ge = 1.73\n",
    "t_eg = 15.0\n",
    "t_ef = 175.95\n",
    "t_fe = 10.0\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000\n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "ge_eg_fit_result3, ge_eg_pcov3 = opt.curve_fit(population_cal, np.concatenate((t, t, t)), np.concatenate((ge_eg_avg_g3, ge_eg_avg_e3, ge_eg_avg_f3)), p0=init_guess, maxfev=5000)\n",
    "\n",
    "ge_eg_result3 = population_cal(np.concatenate((t, t, t)), ge_eg_fit_result3[0], ge_eg_fit_result3[1], ge_eg_fit_result3[2], ge_eg_fit_result3[3], ge_eg_fit_result3[4], ge_eg_fit_result3[5], ge_eg_fit_result3[6])\n",
    "\n",
    "print(ge_eg_fit_result3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "239323b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# g higher than 3\n",
    "path = r'data\\ge_eg_multi_pump\\\\'\n",
    "name = 'Fig_S4_R_'\n",
    "\n",
    "\n",
    "seq_num = 120\n",
    "avg_num = 550\n",
    "tot_time = 30000\n",
    "lim = 10 #400\n",
    "bin_num = 101\n",
    "rotation_angle = -90.0\n",
    "\n",
    "avg_g = np.zeros(seq_num)\n",
    "avg_e = np.zeros(seq_num)\n",
    "avg_f = np.zeros(seq_num)\n",
    "\n",
    "file_num = 1\n",
    "for i in range(file_num):\n",
    "    if np.mod(i, 5) == 0:\n",
    "        print(i)\n",
    "    # prepare data\n",
    "    raw_I_data, raw_Q_data = readdata(path+name+str(i))\n",
    "    I_data = np.mean(raw_I_data[:, 20:80], axis=1)\n",
    "    Q_data = np.mean(raw_Q_data[:, 20:80], axis=1)\n",
    "    \n",
    "    tot_I, tot_Q, fisrt_I, first_Q, result_I, result_Q = separate_data(I_data, Q_data, seq_num, avg_num, rotation_angle)\n",
    "    \n",
    "    # get gaussian center\n",
    "    initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "    g_center, e_center, f_center = gaussian_fit_2d(tot_I, tot_Q, bin_num, lim, initial_guess, plot=False)\n",
    "    \n",
    "    # get population result\n",
    "    g_result, e_result, f_result = get_population(fisrt_I, first_Q, result_I, result_Q, g_center, e_center, f_center, seq_num, avg_num)\n",
    "    tot_index = g_result+e_result+f_result\n",
    "    avg_g = avg_g + g_result/tot_index\n",
    "    avg_e = avg_e + e_result/tot_index\n",
    "    avg_f = avg_f + f_result/tot_index\n",
    "\n",
    "    \n",
    "ge_eg_avg_g4 = avg_g/file_num\n",
    "ge_eg_avg_e4 = avg_e/file_num\n",
    "ge_eg_avg_f4 = avg_f/file_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7398cd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.19258435e-01 7.78933081e-02 2.93654292e-03 1.26688174e+01\n",
      " 5.79799900e+00 3.45480491e+01 8.43604382e+00]\n"
     ]
    }
   ],
   "source": [
    "pg_0 = 1.0\n",
    "pe_0 = 0.0\n",
    "pf_0 = 0.0\n",
    "\n",
    "\n",
    "t_ge = 1.73\n",
    "t_eg = 15.0\n",
    "t_ef = 175.95\n",
    "t_fe = 10.0\n",
    "seq_num = 120\n",
    "avg_num = 500 \n",
    "tot_time = 30000\n",
    "\n",
    "t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "ge_eg_fit_result4, ge_eg_pcov4 = opt.curve_fit(population_cal, np.concatenate((t, t, t)), np.concatenate((ge_eg_avg_g4, ge_eg_avg_e4, ge_eg_avg_f4)), p0=init_guess, maxfev=5000)\n",
    "\n",
    "ge_eg_result4 = population_cal(np.concatenate((t, t, t)), ge_eg_fit_result4[0], ge_eg_fit_result4[1], ge_eg_fit_result4[2], ge_eg_fit_result4[3], ge_eg_fit_result4[4], ge_eg_fit_result4[5], ge_eg_fit_result4[6])\n",
    "\n",
    "print(ge_eg_fit_result4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afdf9d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'), Text(15, 0, '15'), Text(30, 0, '30')]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 506.25x225 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "step = 2\n",
    "\n",
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "fig, ax0 = plt.subplots(1, 2, figsize=(3.375, 1.5), sharex=True)\n",
    "\n",
    "ax = ax0[0]\n",
    "ax1 = ax0[1]\n",
    "\n",
    "\n",
    "ax.scatter(t[0::step], ge_eg_avg_g3[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax.scatter(t[0::step], ge_eg_avg_e3[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax.scatter(t[0::step], ge_eg_avg_f3[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax.plot(t, ge_eg_result3[0:int(len(ge_eg_result3)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax.plot(t, ge_eg_result3[int(len(ge_eg_result3)/3):2*int(len(ge_eg_result3)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax.plot(t, ge_eg_result3[2*int(len(ge_eg_result3)/3):3*int(len(ge_eg_result3)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax.set_ylabel('population', color=label_color)\n",
    "ax.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax.grid(linewidth=0.5)\n",
    "\n",
    "\n",
    "\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_yticks([0.0, 0.5, 1.0])\n",
    "ax.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax1.scatter(t[0::step], ge_eg_avg_g4[0::step], s=markersize, facecolors='none', edgecolors=g_color, zorder=2)\n",
    "ax1.scatter(t[0::step], ge_eg_avg_e4[0::step], s=markersize, facecolors='none', edgecolors=e_color, zorder=2)\n",
    "ax1.scatter(t[0::step], ge_eg_avg_f4[0::step], s=markersize, facecolors='none', edgecolors=f_color, zorder=2)\n",
    "\n",
    "\n",
    "ax1.plot(t, ge_eg_result4[0:int(len(ge_eg_result4)/3)], color=g_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "ax1.plot(t, ge_eg_result4[int(len(ge_eg_result4)/3):2*int(len(ge_eg_result4)/3)], color=e_color, linewidth=linewidth, alpha=alpha, label=r'$| e \\rangle$')\n",
    "ax1.plot(t, ge_eg_result4[2*int(len(ge_eg_result4)/3):3*int(len(ge_eg_result4)/3)], color=f_color, linewidth=linewidth, alpha=alpha, label=r'$| f \\rangle$')\n",
    "\n",
    "ax1.set_xlabel(r'time ($\\mu$s)', color=label_color)\n",
    "ax1.set_ylabel('population', color=label_color)\n",
    "ax1.tick_params(axis='both', which='major', colors=label_color)\n",
    "ax1.grid(linewidth=0.5)\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_yticks([0.0, 0.5, 1.0])\n",
    "ax1.set_yticklabels([0.0, 0.5, 1.0])\n",
    "\n",
    "ax1.set_xlim([0, 30])\n",
    "ax1.set_xticks([0, 15, 30])\n",
    "ax1.set_xticklabels([0, 15, 30])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7d9bc4",
   "metadata": {},
   "source": [
    "## Generate Supplement Figure S5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9269d69e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.526121005\n",
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      "12.526621005\n",
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      "12.527121005\n",
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      "12.528121005000001\n",
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      "12.529121005\n",
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    }
   ],
   "source": [
    "path = r'data\\pump_rate_vs_voltage\\\\'\n",
    "sum_pump_freqs = np.linspace(12.526121005, 12.536121005, 21)\n",
    "sum_pump_amplitdues = np.linspace(0.5, 2.75, 10)\n",
    "sum_pump_results = np.zeros((len(sum_pump_freqs), len(sum_pump_amplitdues), 7))\n",
    "sum_processed_data = np.zeros((len(sum_pump_freqs), len(sum_pump_amplitdues), 3, 120))\n",
    "\n",
    "\n",
    "# get the processed data\n",
    "for i in range(len(sum_pump_freqs)):\n",
    "    print(sum_pump_freqs[i])\n",
    "    for j in range(len(sum_pump_amplitdues)):\n",
    "        temp_name = '001_ge_piby2_ef_piby2_msmt_prep_g_sum_pump_on_{}_V_pump_freq_{}GHz_msmt_noweight_'.format(sum_pump_amplitdues[j], str(round(sum_pump_freqs[i], 9)))\n",
    "        seq_num = 120\n",
    "        avg_num = 550\n",
    "        tot_time = 120000\n",
    "        lim = 10\n",
    "        bin_num = 101\n",
    "        rotation_angle = -90.0\n",
    "        filenum = 3#15\n",
    "        initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "\n",
    "\n",
    "        temp_g_sum, temp_e_sum, temp_f_sum = batch_process(path, temp_name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)\n",
    "        sum_processed_data[i, j, 0, :] = temp_g_sum\n",
    "        sum_processed_data[i, j, 1, :] = temp_e_sum\n",
    "        sum_processed_data[i, j, 2, :] = temp_f_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8f577e9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.526121005\n",
      "12.526621005\n",
      "12.527121005\n",
      "12.527621005\n",
      "12.528121005000001\n",
      "12.528621005\n",
      "12.529121005\n",
      "12.529621005000001\n",
      "12.530121005\n",
      "12.530621005\n",
      "12.531121005\n",
      "12.531621005\n",
      "12.532121005\n",
      "12.532621005\n",
      "12.533121005\n",
      "12.533621005\n",
      "12.534121005\n",
      "12.534621005\n",
      "12.535121005\n",
      "12.535621005\n",
      "12.536121005\n"
     ]
    }
   ],
   "source": [
    "# fit the processed data\n",
    "\n",
    "for i in range(len(sum_pump_freqs)):\n",
    "    print(sum_pump_freqs[i])\n",
    "    for j in range(len(sum_pump_amplitdues)):\n",
    "        pg_0 = 1.0\n",
    "        pe_0 = 0.0\n",
    "        pf_0 = 0.0\n",
    "\n",
    "        try:\n",
    "            t_ge = 8.0\n",
    "            t_eg = 12\n",
    "            t_ef = 51\n",
    "            t_fe = 8\n",
    "            seq_num = 120\n",
    "            avg_num = 500 \n",
    "            tot_time = 30000 \n",
    "\n",
    "            t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "            fit_init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "            \n",
    "            temp_sum_fit_result, temp_sum_result = fit_pump_data(t, sum_processed_data[i, j, 0], sum_processed_data[i, j, 1], sum_processed_data[i, j, 2], fit_init_guess)\n",
    "        except:\n",
    "            t_ge = 1.0\n",
    "            t_eg = 12\n",
    "            t_ef = 51\n",
    "            t_fe = 8\n",
    "            seq_num = 120\n",
    "            avg_num = 500 \n",
    "            tot_time = 30000 \n",
    "\n",
    "            t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "            fit_init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "            temp_sum_fit_result, temp_sum_result = fit_pump_data(t, sum_processed_data[i, j, 0], sum_processed_data[i, j, 1], sum_processed_data[i, j, 2], fit_init_guess)\n",
    "\n",
    "        #         print(temp_sum_fit_result)\n",
    "        sum_pump_results[i, j] = temp_sum_fit_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9cf6cbc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_pump_optimal_rate = np.zeros((len(sum_pump_amplitdues), 2))\n",
    "for i in range(len(sum_pump_amplitdues)):\n",
    "    temp_index = np.argmin(sum_pump_results[:, i, 3])\n",
    "    sum_pump_optimal_rate[i, 0] = sum_pump_results[temp_index, i, 3]\n",
    "    sum_pump_optimal_rate[i, 1] = sum_pump_results[temp_index, i, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "851fd33a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
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     ]
    }
   ],
   "source": [
    "# path = r'C:\\Users\\caoxi\\Box\\Chemical potential\\ge_conversion\\sweep_0.25V_2.5V\\\\'\n",
    "path = r'data\\pump_rate_vs_voltage\\conversion\\\\'\n",
    "\n",
    "conv_pump_freqs = np.linspace(3.4835290945, 3.493529094, 21)\n",
    "conv_pump_amplitdues = np.linspace(0.25, 2.5, 10)\n",
    "conv_pump_results = np.zeros((len(conv_pump_freqs), len(conv_pump_amplitdues), 7))\n",
    "conv_processed_data = np.zeros((len(conv_pump_freqs), len(conv_pump_amplitdues), 3, 120))\n",
    "\n",
    "for i in range(len(conv_pump_freqs)):\n",
    "    print(i)\n",
    "    for j in range(len(conv_pump_amplitdues)):\n",
    "        if (np.array_equal(conv_processed_data[i, j, 0, :], np.zeros(120))) and (np.array_equal(conv_processed_data[i, j, 1, :], np.zeros(120))) and (np.array_equal(conv_processed_data[i, j, 2, :], np.zeros(120))):\n",
    "            temp_name = '001_ge_piby2_ef_piby2_msmt_prep_e_conversion_pump_{}_V_{}GHz_1.0V_msmt_noweight_0dBattenconversion_TWPA_7.72GHz_-15.2dBm'.format(conv_pump_amplitdues[j], str(round(conv_pump_freqs[i], 9)))\n",
    "            seq_num = 120\n",
    "            avg_num = 550\n",
    "            tot_time = 30000\n",
    "            lim = 10\n",
    "            bin_num = 101\n",
    "            rotation_angle = -90.0\n",
    "            filenum = 3\n",
    "            initial_guess = (100, 100, 100, -3, 5, -4, -2, 0, -5, 2, 2, 2, 2, 2, 2)\n",
    "            \n",
    "            success = False\n",
    "            try_count = 0\n",
    "            while not success:\n",
    "                try:\n",
    "                    temp_g_conv, temp_e_conv, temp_f_conv = batch_process(path, temp_name, filenum, lim, bin_num, rotation_angle, seq_num, avg_num)\n",
    "                    conv_processed_data[i, j, 0, :] = temp_g_conv\n",
    "                    conv_processed_data[i, j, 1, :] = temp_e_conv\n",
    "                    conv_processed_data[i, j, 2, :] = temp_f_conv\n",
    "                    success = True\n",
    "                except:\n",
    "                    print(\"Some error\")\n",
    "                    try_count = try_count + 1\n",
    "                    if try_count > 10:\n",
    "                        success = True\n",
    "\n",
    "        else:\n",
    "            pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e23b21ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.4835290945\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xicao\\AppData\\Local\\Temp\\ipykernel_23240\\64586804.py:215: RuntimeWarning: overflow encountered in exp\n",
      "  pg = v[0, 0]*c_coeff[0]*np.exp(w[0]*t) + v[0, 1]*c_coeff[1]*np.exp(w[1]*t) + v[0, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
      "C:\\Users\\xicao\\AppData\\Local\\Temp\\ipykernel_23240\\64586804.py:216: RuntimeWarning: overflow encountered in exp\n",
      "  pe = v[1, 0]*c_coeff[0]*np.exp(w[0]*t) + v[1, 1]*c_coeff[1]*np.exp(w[1]*t) + v[1, 2]*c_coeff[2]*np.exp(w[2]*t)\n",
      "C:\\Users\\xicao\\AppData\\Local\\Temp\\ipykernel_23240\\64586804.py:217: RuntimeWarning: overflow encountered in exp\n",
      "  pf = v[2, 0]*c_coeff[0]*np.exp(w[0]*t) + v[2, 1]*c_coeff[1]*np.exp(w[1]*t) + v[2, 2]*c_coeff[2]*np.exp(w[2]*t)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.4840290944750003\n",
      "3.48452909445\n",
      "3.485029094425\n",
      "3.4855290944\n",
      "3.486029094375\n",
      "3.4865290943500002\n",
      "3.487029094325\n",
      "3.4875290943\n",
      "3.488029094275\n",
      "3.48852909425\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Cannot cast array data from dtype('complex128') to dtype('float64') according to the rule 'safe'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;31mTypeError\u001b[0m: Cannot cast array data from dtype('complex128') to dtype('float64') according to the rule 'safe'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here\n",
      "3.489029094225\n",
      "3.4895290942\n",
      "3.490029094175\n",
      "3.49052909415\n",
      "3.491029094125\n",
      "3.4915290941\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Cannot cast array data from dtype('complex128') to dtype('float64') according to the rule 'safe'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;31mTypeError\u001b[0m: Cannot cast array data from dtype('complex128') to dtype('float64') according to the rule 'safe'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here\n",
      "3.492029094075\n",
      "3.49252909405\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Cannot cast array data from dtype('complex128') to dtype('float64') according to the rule 'safe'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;31mTypeError\u001b[0m: Cannot cast array data from dtype('complex128') to dtype('float64') according to the rule 'safe'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here\n",
      "3.4930290940249997\n",
      "3.493529094\n"
     ]
    }
   ],
   "source": [
    "t_ge_guess = np.linspace(1.0, 10, 11)\n",
    "\n",
    "for i in range(len(conv_pump_freqs)):\n",
    "    print(conv_pump_freqs[i])\n",
    "    for j in range(len(conv_pump_amplitdues)):\n",
    "        k = 0\n",
    "        fit_pass = False\n",
    "        while not fit_pass:\n",
    "            try:\n",
    "                pg_0 = 0.0\n",
    "                pe_0 = 1.0\n",
    "                pf_0 = 0.0\n",
    "\n",
    "\n",
    "                t_ge = 9.0\n",
    "                t_eg = t_ge_guess[k]\n",
    "                t_ef = 51\n",
    "                t_fe = 8\n",
    "                seq_num = 120\n",
    "                avg_num = 500 \n",
    "                tot_time = 30000 \n",
    "\n",
    "                t = (np.linspace(0, tot_time, seq_num+1) + 150 )[0:seq_num]*1e-3\n",
    "                fit_init_guess = (pg_0, pe_0, pf_0, t_ge, t_eg, t_ef, t_fe)\n",
    "\n",
    "                temp_conv_fit_result, temp_conv_result = fit_pump_data(t, conv_processed_data[i, j, 0], conv_processed_data[i, j, 1], conv_processed_data[i, j, 2], fit_init_guess)\n",
    "                fit_pass = True\n",
    "            except:\n",
    "                # if see fitting errors, move the initial guess a little to have good fit\n",
    "                print('here')\n",
    "                fit_pass = False\n",
    "                k = k + 1\n",
    "                \n",
    "            \n",
    "            \n",
    "        conv_pump_results[i, j] = temp_conv_fit_result\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "911eda77",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_pump_optimal_rate = np.zeros((len(conv_pump_amplitdues), 2))\n",
    "for i in range(len(conv_pump_amplitdues)):\n",
    "    temp_index = np.argmin(conv_pump_results[:, i, 4])\n",
    "    conv_pump_optimal_rate[i, 0] = conv_pump_results[temp_index, i, 3]\n",
    "    conv_pump_optimal_rate[i, 1] = conv_pump_results[temp_index, i, 4]\n",
    "# plt.plot(conv_pump_amplitdues[1::], 1.0/conv_pump_optimal_rate[1::, 1]/2/np.pi, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "ed0b1c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_pump_amplitdues, 1.0/sum_pump_optimal_rate[:, 0]/2/np.pi\n",
    "\n",
    "sum_rate_vs_amp_fit, sum_rate_vs_amp_fit_stats = np.polynomial.polynomial.polyfit(sum_pump_amplitdues, 1.0/sum_pump_optimal_rate[:, 0]/2/np.pi, 2,full=True)\n",
    "sum_rate_vs_amp_fit_result = sum_rate_vs_amp_fit[2]*sum_pump_amplitdues**2 + sum_rate_vs_amp_fit[1]*sum_pump_amplitdues + sum_rate_vs_amp_fit[0]\n",
    "# plt.plot(sum_pump_amplitdues, 1.0/sum_pump_optimal_rate[:, 0]/2/np.pi, 'o')\n",
    "# plt.plot(sum_pump_amplitdues, sum_rate_vs_amp_fit_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "246c0ef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_rate_vs_amp_fit, conv_rate_vs_amp_fit_stats = np.polynomial.polynomial.polyfit(conv_pump_amplitdues[1::], 1.0/conv_pump_optimal_rate[1::, 1]/2/np.pi, 2,full=True)\n",
    "conv_rate_vs_amp_fit_result = conv_rate_vs_amp_fit[2]*conv_pump_amplitdues[1::]**2 + conv_rate_vs_amp_fit[1]*conv_pump_amplitdues[1::] + conv_rate_vs_amp_fit[0]\n",
    "# plt.plot(conv_pump_amplitdues[1::], 1.0/conv_pump_optimal_rate[1::, 1]/2/np.pi, 'o')\n",
    "# plt.plot(conv_pump_amplitdues[1::], conv_rate_vs_amp_fit_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "30c2bed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\Gamma_{ge}/2\\\\pi$ kHz')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 506.25x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_pump_color = 'magenta'\n",
    "sum_pump_color = 'purple'\n",
    "\n",
    "label_color = 'black'\n",
    "setup_figure(sns_style='whitegrid', rcparams={'font.size':8})\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, figsize=(3.375, 2), sharex=True)\n",
    "\n",
    "ax[0].scatter(conv_pump_amplitdues[1::], 1.0/conv_pump_optimal_rate[1::, 1]/2/np.pi, s=5, facecolors='white', edgecolors=conv_pump_color, zorder=2)\n",
    "ax[0].plot(conv_pump_amplitdues[1::], conv_rate_vs_amp_fit_result, color=conv_pump_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "\n",
    "\n",
    "# ax[0].plot(conv_pump_amplitdues[1::], 1.0/conv_pump_optimal_rate[1::, 1]/2/np.pi, 'o')\n",
    "# ax[0].plot(conv_pump_amplitdues[1::], conv_rate_vs_amp_fit_result)\n",
    "ax[0].set_xlim([0.5, 2.5])\n",
    "ax[0].set_xticks([0.5, 1.5, 2.5])\n",
    "ax[0].set_xticklabels([0.5, 1.5, 2.5], color=label_color)\n",
    "ax[0].set_ylim([0, 0.15])\n",
    "ax[0].set_yticks([0.0, 0.075, 0.15])\n",
    "ax[0].set_yticklabels(['0', '75', '150'], color=label_color)\n",
    "ax[0].set_ylabel(r'$\\Gamma_{eg}/2\\pi$ kHz', color=label_color)\n",
    "\n",
    "ax[1].scatter(sum_pump_amplitdues, 1.0/sum_pump_optimal_rate[:, 0]/2/np.pi, s=5, facecolors='white', edgecolors=sum_pump_color, zorder=2)\n",
    "ax[1].plot(sum_pump_amplitdues, sum_rate_vs_amp_fit_result, color=sum_pump_color, linewidth=linewidth, alpha=alpha, label=r'$| g \\rangle$')\n",
    "# ax[1].plot(sum_pump_amps, 1.0/sum_pump_optimal_rate[:, 0]/2/np.pi, 'o')\n",
    "# ax[1].plot(sum_pump_amps, sum_rate_vs_amp_fit_result)\n",
    "ax[1].set_xlim([0.5, 2.5])\n",
    "ax[1].set_xticks([0.5, 1.5, 2.5])\n",
    "ax[1].set_xticklabels([0.5, 1.5, 2.5], color=label_color)\n",
    "ax[1].set_ylim([0, 0.4])\n",
    "ax[1].set_yticks([0.0, 0.2, 0.4])\n",
    "ax[1].set_yticklabels([0, 200, 400], color=label_color)\n",
    "ax[1].set_xlabel('drive amplitude (V)', color=label_color)\n",
    "ax[1].set_ylabel(r'$\\Gamma_{ge}/2\\pi$ kHz', color=label_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58e63238",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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